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AI Security, Ethics and GovernancemediumMultiple ChoiceObjective-mapped

AI0-001 AI Security, Ethics and Governance Practice Question

A healthcare organization uses an AI model to predict patient readmission risk. To comply with patient privacy regulations, they apply differential privacy during training. What is the primary trade-off of using differential privacy?

⚠ Common exam trap

The AI0-001 exam often tests the misconception that differential privacy primarily reduces bias or improves fairness, when in fact its core trade-off is accuracy for privacy, and fairness can be negatively impacted by the added noise.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Reduced model accuracy for increased privacy

Differential privacy works by adding calibrated noise to the training process or model outputs, which directly reduces the model's accuracy in exchange for a quantifiable privacy guarantee (e.g., ε-differential privacy). This trade-off is fundamental: stronger privacy (lower ε) requires more noise, which degrades predictive performance. The healthcare organization must balance the need to protect patient data against the clinical utility of accurate readmission predictions.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Increased training time for reduced bias

    Why it's wrong here

    Training time may increase, but bias reduction is not the primary trade-off.

  • Lower interpretability for higher fairness

    Why it's wrong here

    Interpretability and fairness are not directly traded off with differential privacy.

  • Faster inference for lower memory usage

    Why it's wrong here

    Inference speed and memory are not significantly affected.

  • Reduced model accuracy for increased privacy

    Why this is correct

    Noise injection lowers accuracy but bounds privacy loss.

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.